Enhancement of 18F-Fluorodeoxyglucose PET Image Quality by Deep-Learning-Based Image Reconstruction Using Advanced Intelligent Clear-IQ Engine in Semiconductor-Based PET/CT Scanners

Yamagiwa, Ken; Tsuchiya, Junichi; Yokoyama, Kota; Watanabe, Ryosuke; Kimura, Koichiro; Kishino, Mitsuhiro; Tateishi, Ukihide

doi:10.3390/diagnostics12102500

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…Our results showed that the DL denoising technique can maintain or improve the diagnostic image quality of low [ 18 F]FDG PET in patients with lymphoma. These results were consistent with the finding of previous studies ( 21 , 28 ). Furthermore, the quantitative consistency of SUV between LD PET with DL denoising and RD PET was demonstrated in a larger population.…”

Section: Discussionsupporting

confidence: 94%

Low 18F-fluorodeoxyglucose dose positron emission tomography assisted by a deep-learning image-denoising technique in patients with lymphoma

Yan,

Wang,

et al. 2024

Quant Imaging Med Surg

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Background Patients with lymphoma receive multiple positron emission tomography/computed tomography (PET/CT) exams for monitoring of the therapeutic response. With PET imaging, a reduced level of injected fluorine-18 fluorodeoxyglucose ([ 18 F]FDG) activity can be administered while maintaining the image quality. In this study, we investigated the efficacy of applying a deep learning (DL) denoising-technique on image quality and the quantification of metabolic parameters and Deauville score (DS) of a low [ 18 F]FDG dose PET in patients with lymphoma. Methods This study retrospectively enrolled 62 patients who underwent [ 18 F]FDG PET scans. The low-dose (LD) data were simulated by taking a 50% duration of routine-dose (RD) PET list-mode data in the reconstruction, and a U-Net-based denoising neural network was applied to improve the images of LD PET. The visual image quality score (1 = undiagnostic, 5 = excellent) and DS were assessed in all patients by nuclear radiologists. The maximum, mean, and standard deviation (SD) of the standardized uptake value (SUV) in the liver and mediastinum were measured. In addition, lesions in some patients were segmented using a fixed threshold of 2.5, and their SUV, metabolic tumor volume (MTV), and tumor lesion glycolysis (TLG) were measured. The correlation coefficient and limits of agreement between the RD and LD group were analyzed. Results The visual image quality of the LD group was improved compared with the RD group. The DS was similar between the RD and LD group, and the negative (DS 1–3) and positive (DS 4–5) results remained unchanged. The correlation coefficients of SUV in the liver, mediastinum, and lesions were all >0.85. The mean differences of SUV max and SUV mean between the RD and LD groups, respectively, were 0.22 [95% confidence interval (CI): –0.19 to 0.64] and 0.02 (95% CI: –0.17 to 0.20) in the liver, 0.13 (95% CI: –0.17 to 0.42) and 0.02 (95% CI: –0.12 to 0.16) in the mediastinum, and –0.75 (95% CI: –3.42 to 1.91), and –0.13 (95% CI: –0.57 to 0.31) in lesions. The mean differences in MTV and TLG were 0.85 (95% CI: –2.27 to 3.98) and 4.06 (95% CI: –20.53 to 28.64) between the RD and LD groups. Conclusions The DL denoising technique enables accurate tumor assessment and quantification with LD [ 18 F]FDG PET imaging in patients with lymphoma.

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Section: Discussionsupporting

confidence: 94%

Low 18F-fluorodeoxyglucose dose positron emission tomography assisted by a deep-learning image-denoising technique in patients with lymphoma

Yan,

Wang,

et al. 2024

Quant Imaging Med Surg

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Denoising approaches by SubtlePET™ artificial intelligence in positron emission tomography (PET) for clinical routine application

De Summa,

Ruggiero,

Spinosa

et al. 2024

Clin Transl Imaging

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Deep Learning-Based Image Quality Improvement in Digital Positron Emission Tomography for Breast Cancer

et al. 2023

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We investigated whether 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography images restored via deep learning (DL) improved image quality and affected axillary lymph node (ALN) metastasis diagnosis in patients with breast cancer. Using a five-point scale, two readers compared the image quality of DL-PET and conventional PET (cPET) in 53 consecutive patients from September 2020 to October 2021. Visually analyzed ipsilateral ALNs were rated on a three-point scale. The standard uptake values SUVmax and SUVpeak were calculated for breast cancer regions of interest. For “depiction of primary lesion”, reader 2 scored DL-PET significantly higher than cPET. For “noise”, “clarity of mammary gland”, and “overall image quality”, both readers scored DL-PET significantly higher than cPET. The SUVmax and SUVpeak for primary lesions and normal breasts were significantly higher in DL-PET than in cPET (p < 0.001). Considering the ALN metastasis scores 1 and 2 as negative and 3 as positive, the McNemar test revealed no significant difference between cPET and DL-PET scores for either reader (p = 0.250, 0.625). DL-PET improved visual image quality for breast cancer compared with cPET. SUVmax and SUVpeak were significantly higher in DL-PET than in cPET. DL-PET and cPET exhibited comparable diagnostic abilities for ALN metastasis.

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Enhancement of 18F-Fluorodeoxyglucose PET Image Quality by Deep-Learning-Based Image Reconstruction Using Advanced Intelligent Clear-IQ Engine in Semiconductor-Based PET/CT Scanners

Cited by 4 publications

References 30 publications

Low 18F-fluorodeoxyglucose dose positron emission tomography assisted by a deep-learning image-denoising technique in patients with lymphoma

Low 18F-fluorodeoxyglucose dose positron emission tomography assisted by a deep-learning image-denoising technique in patients with lymphoma

Denoising approaches by SubtlePET™ artificial intelligence in positron emission tomography (PET) for clinical routine application

Deep Learning-Based Image Quality Improvement in Digital Positron Emission Tomography for Breast Cancer

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